1. Field of the Invention
The present invention relates to data processing and, in particular, to failure prediction. Still more particularly, the present invention provides a method, apparatus, and program for using data mining, spatial analysis, linear programming, narrowcasting, data warehousing, visualization, and text mining in a failure prediction system.
2. Background of the Invention
Product failures may lead to various consequences. Typically, when a product defect is discovered, the product is recalled. However, the product defect may be discovered only after catastrophic consequences are suffered. For example, an infant car seat may be recalled only after numerous injuries or possibly deaths. Preferably product defects and their subsequent repair under warranty would trigger timely actions that would minimize the liability and expenses associated with the defect.
Product failures can also be costly in public relations for a manufacturer. Particularly when the safety of consumers is threatened, public perception may be damaged. Even if only one part or model is found to be defective, trust and loyalty in a brand name may be destroyed. Thus, great cost may be expended in restoring the trust of consumers.
Furthermore, a product failure may have an effect on other related companies. For example, an automobile manufacturer may factory install a particular brand and model of tires on automobiles. If that particular model of tires has a defect, failures could result in injuries and possibly loss of life. Both the tire manufacturer and automobile manufacturer may find themselves buried in law suits, recalls, and public relation problems.
Therefore, it would be advantageous to provide an improved system for predicting failures to avoid unnecessary risk to the public and inestimable cost to the manufacturer.
The present invention provides a system and method to predict possible product failures with automatic notification of people as well as systems. The present invention integrates data mining, spatial analysis, linear programming, narrowcasting, data warehousing, visualization, and text mining. As a result, failure conditions, attributes, complaints, locations, consequences, and sequence of events are analyzed using data mining technologies. This data is fed into an optimization module that assesses the efficiency of the failure process such that failures can be assessed as to their priority. These priorities are then used to feed a triggering engine that triggers notification of systems and individuals using narrowcasting technology. This system is one that allows early warning of potential problems to occur and integrates data from call centers, legacy systems, retailers, manufacturers, vendor supplied parts, and transportation of parts.